[1]} propose the Integration Flow (IF) mechanism to improve the training efficiency.
As illustrated in Figure REF  (Left), an IF layer performs reasoning in parallel for each question, and then refines the reasoning results across different dialog turns.
Given the input question and previous questions, each represented using their integrated contextual vectors,
the IF layer generates a history-aware integrated contextual vector for each passage word using a LSTM.
So, the entire integrated contextual vectors for answering previous questions can be used to answer the input question.
As illustrated in Figure REF  (Right), FlowQA employs multiple IF layers on top of contextual encoding, inter-weaved with attention (first on question, then on context itself), to generate the history-aware integrated contextual vectors.
The answer is predicted on the integrated context vectors.
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